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AI models do not create broad economic or strategic advantage simply by existing. Hans Tung, managing partner at Notable Capital, argues that the infrastructure to run and distribute them matters just as much: “The models that win are the ones that are available.” In a Fortune feature published October 13, 2026, he compares today’s AI buildout with earlier industrial shifts powered by steel steamships and railroads.
What is the Industrial Revolution analogy?
The comparison is not that data centers are literally railroads, or that AI repeats the history of steamships. It is that a transformative technology can have limited practical reach until a physical system makes it usable at scale. Steel ships changed what maritime transport could do; railroads moved people and resources over land; data centers supply the compute needed to run AI models and make them accessible to users.
Tung’s argument, as reported by Fortune, puts deployment and distribution at the center of technological advantage. A capable model that cannot be run reliably or made available to people and businesses may matter less in practice than a model supported by infrastructure that can reach them.
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Why bring steel ships and railroads into the discussion?
Steel ships: invention paired with industrial capacity
Fortune reports that 80% of the world’s ships came from British shipyards in 1890. The figure illustrates the feature’s focus on the industrial base behind a technology: building and operating advanced ships at scale required more than the idea of a steel vessel. The underlying source for that historical percentage is not identified in the reporting available here, so it is best understood as Fortune’s reported figure rather than independently verified data.
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Railroads: moving resources and extending reach
Tung’s railroad analogy concerns the ability to move resources and extend infrastructure’s reach. He invokes the North’s railroad network during the Civil War as an example of how transport capacity can support a larger system. The parallel to AI is the need to supply compute where it is required and make models available at scale. It is an analogy about infrastructure’s enabling role, not evidence by itself that current AI capacity determines a geopolitical outcome.
How does the analogy apply to AI infrastructure?
Data centers house the computing equipment used to train and run AI systems. In Tung’s framing, that capacity is part of the technology’s delivery network: it affects whether models can be accessed and used, not just whether they have been developed. The comparison is most useful across four questions:
- Buildout: What scale of infrastructure is being assembled?
- Delivery: Can the system move or supply the resources the technology needs?
- Execution: Can the infrastructure be built and operated effectively?
- Availability: Does it make the technology reachable to users and organizations?
Those questions help explain why a discussion of AI leadership can include power, data-center capacity and deployment alongside models. They do not establish that infrastructure alone decides who leads: the analogy does not account for every factor that shapes AI development, adoption or national advantage.
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Fortune’s October 13, 2026 feature reported approximately 24 gigawatts of working data-center compute capacity in China and at least 47 gigawatts in the United States, described as a conservative estimate. It also reported that a Columbia Business School professor estimated AI infrastructure could account for an average of 3.6% of U.S. GDP each year until 2032.
| Reported figure | Geography or period | Attribution and qualification |
|---|---|---|
| About 24 GW of working data-center compute capacity | China | Estimate reported by Fortune on October 13, 2026; capacity definitions and underlying source are not independently established here. |
| At least 47 GW of data-center compute capacity | United States | Fortune described this as a conservative estimate on October 13, 2026; capacity definitions and underlying source are not independently established here. |
| Average of 3.6% of GDP per year through 2032 | United States | Estimate attributed by Fortune to a Columbia Business School professor; the professor and underlying estimate are not identified in the reporting available here. |
These numbers are reported claims, not a like-for-like, independently validated measurement. Data-center capacity can change quickly, and the figures may depend on how “working” capacity or infrastructure investment is defined. The GDP estimate is also an attributed projection, not a confirmed annual outcome.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the comparison can—and cannot—tell us
The analogy’s central insight is practical: invention and availability are different stages. An AI model’s capabilities may not translate into broad use unless computing infrastructure can support deployment and access. Historical examples make that infrastructure question easier to see, but they do not prove that AI will follow the same path as steamships or railroads.
Fortune’s reported capacity and GDP figures add scale to the argument, but they should be read with their qualifications. They provide context for the investment and infrastructure discussion; they do not, on their own, settle which country or company will turn AI into the greatest economic or strategic advantage.
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